Bibliographic record
Abstract
Accelerated testing in environments aggressive for the specific material have shown that fastener threads that are rolled after strengthening heat treatments have improved resistance to SCC initiation. For example, intergranular SCC was produced in one day when machined (cut) threads of high strength steel (ASTM A193 B-7 and A354 Grade 8) were exposed to an aggressive aqueous environment containing 8 weight % boiling ammonium nitrate and stressed to about 40% of the steel's yield strength (120 ksi, 827 MPa). In similar testing conditions, fasteners that had threads rolled before heat-treatment (quench and temper) had similar susceptibility to SCC. However, threads rolled after strengthening, exhibited no SCC after a week of exposure, even when stressed to 100% of the B-7 alloy yield strength. Similarly, intergranular SCC was produced in less than one day when machined (cut) threads of nickel-base alloys (X-750 and aged 625) were exposed to an aggressive 750 F doped steam environment (containing 100 ppm of chloride, fluoride, sulfate, nitrate and a controlled hydrogen overpressure) and stressed to about 80% of the alloy yield strength (117 ksi, 807 MPa). In similar testing conditions, threads rolled after strengthening exhibited no SCC after 50 days of exposure. This beneficial effect of the optimum thread rolling process (i.e., threads rolled after strengthening) is due to the retention of large residual compressive stresses in the thread roots (notches) which mitigate the applied notch tensile stresses resulting from joint design pre-loads. use of these material specific aggressive environments can provide an accelerated test to verify that threads were produced by the optimum thread rolling process. These tests could support fastener acceptance criteria or failure analysis of fasteners with unknown or uncertain manufacturing processes. The optimum process effects may not always be detected by more conventional methods (e.g., metallography or hardness testing).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".